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Deepfake: definitions, performance metrics and standards, datasets, and a meta-review

Altuncu, Enes, Franqueira, Virginia N. L., Li, Shujun (2024) Deepfake: definitions, performance metrics and standards, datasets, and a meta-review. Frontiers in Big Data, 7 . Article Number 1400024. ISSN 2624-909X. (doi:10.3389/fdata.2024.1400024) (KAR id:107255)

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Official URL:
https://doi.org/10.3389/fdata.2024.1400024

Abstract

Recent advancements in AI, especially deep learning, have contributed to a significant increase in the creation of new realistic-looking synthetic media (video, image, and audio) and manipulation of existing media, which has led to the creation of the new term “deepfake.” Based on both the research literature and resources in English, this paper gives a comprehensive overview of deepfake, covering multiple important aspects of this emerging concept, including (1) different definitions, (2) commonly used performance metrics and standards, and (3) deepfake-related datasets. In addition, the paper also reports a meta-review of 15 selected deepfake-related survey papers published since 2020, focusing not only on the mentioned aspects but also on the analysis of key challenges and recommendations. We believe that this paper is the most comprehensive review of deepfake in terms of the aspects covered.

Item Type: Article
DOI/Identification number: 10.3389/fdata.2024.1400024
Uncontrolled keywords: datasets, deepfake, performance metrics, survey, standards, definition
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (https://ror.org/00xkeyj56)
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 09 Oct 2024 13:57 UTC
Last Modified: 05 Nov 2024 13:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/107255 (The current URI for this page, for reference purposes)

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